(Senior) Machine Learning Engineer

Our client is a pioneering European deep-tech organisation operating at the forefront of AI and advanced computation. Their innovative technology is transforming how artificial intelligence is developed and deployed driving faster, more efficient, and more sustainable models across industries.

We are seeking a Senior Machine Learning Engineer (Product) to join their growing team. Lead the design and implementation of end-to-end data and model pipelines, from dataset creation and validation to training, fine-tuning, and deployment.

Develop rigorous model evaluation frameworks, implement statistical testing, and establish continuous performance monitoring.

Scale training and inference processes through distributed compute and algorithmic optimisation.

Apply post-training and alignment methods (SFT, reinforcement learning, or preference-based optimisation) to improve model behaviour and quality.

Collaborate closely with cross-functional teams to ensure production-grade delivery and uphold engineering best practices.

Mentor junior engineers and contribute to continuous technical improvement across the team.

Bachelor’s degree in Computer Science, Mathematics, Physics, Data Science, or a related field (Master’s or PhD preferred).

Proven experience in machine learning engineering, with a strong background in model development and deployment.

Expertise in Python and the modern ML ecosystem (e.g., PyTorch, NumPy, Pandas).

Hands-on experience working with large-scale datasets and distributed training environments.

Proficiency with GPU-based workflows, Docker, and cloud orchestration tools.

Deep understanding of LLMs or other large-scale model architectures, training dynamics, and evaluation methods.

Ability to translate complex research concepts into scalable, production-level implementations.

Experience with model compression (quantisation, pruning, NAS) or post-training optimisation (SFT, RLHF).

Strong background in statistical evaluation and performance analysis.

Prior work on foundational model training or scaling distributed compute systems.

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